Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of deep learning programs together to maximize the efficiency and your productivity.
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Prerequisites
This package supports Linux and Windows platforms. You may also want to check: - mxnet-cu101 with CUDA-10.1 support. - mxnet-cu100 with CUDA-10.0 support. - mxnet-cu100mkl with CUDA-10.0 support and MKLDNN support. - mxnet-cu92 with CUDA-9.2 support. - mxnet-cu92mkl with CUDA-9.2 support and MKLDNN support. - mxnet-cu91 with CUDA-9.1 support. - mxnet-cu91mkl with CUDA-9.1 support and MKLDNN support. - mxnet-cu90 with CUDA-9.0 support. - mxnet-cu90mkl with CUDA-9.0 support and MKLDNN support. - mxnet-cu80 with CUDA-8.0 support. - mxnet-cu80mkl with CUDA-8.0 support and MKLDNN support. - mxnet-cu75 with CUDA-7.5 support. - mxnet-cu75mkl with CUDA-7.5 support and MKLDNN support. - mxnet-mkl with MKLDNN support. - mxnet.
To download CUDA, check CUDA download. For more instructions, check CUDA Toolkit online documentation.
To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check Installing MXNet for instructions on building from source.
Installation
To install:
pip install mxnet-cu101mkl
Release files for mxnet-cu101mkl 1.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mxnet_cu101mkl-1.4.1-py2.py3-none-manylinux1_x86_64.whl | Python 3, Python 2 | none | Linux glibc 2.5+ x86-64 | Details |
Release files / mxnet_cu101mkl-1.4.1-py2.py3-none-manylinux1_x86_64.whl
| Download URL | mxnet_cu101mkl-1.4.1-py2.py3-none-manylinux1_x86_64.whl |
|---|---|
| Size | 584.0 MB |
| Tags | Linux glibc 2.5+ x86-64 Python 2 Python 3 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/36.2.0 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/2.7.6
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